diff --git a/paper-2015/Fig6_NetMultiscale/Fig6BCDE.py b/paper-2015/Fig6_NetMultiscale/Fig6BCDE.py
index 066e68d1365dd3a16cff3487968c33841108ab4f..6d79570059bf7de6714520a430b4a08e75779212 100644
--- a/paper-2015/Fig6_NetMultiscale/Fig6BCDE.py
+++ b/paper-2015/Fig6_NetMultiscale/Fig6BCDE.py
@@ -309,7 +309,7 @@ def connectDetailedNeuron():
x.vec.weight = nprand.rand( exc.numEntries ) * excWeightMax
#x.parent.tick = 4
x.parent.parent.tick = 4
- print '+',
+ print('+', end=' ')
totGluWt += sum(x.vec.weight) * x.parent.parent.Gbar
seed = excSeed
@@ -325,7 +325,7 @@ def connectDetailedNeuron():
x.vec.weight = nprand.rand( exc.numEntries ) * nmdaWeightMax
#x.parent.tick = 4
x.parent.parent.tick = 4
- print '*',
+ print('*', end=' ')
totNMDAWt += sum(x.vec.weight) * x.parent.parent.Gbar
seed = inhSeed
@@ -340,11 +340,11 @@ def connectDetailedNeuron():
x.vec.weight = nprand.rand( inh.numEntries ) * inhWeightMax
#x.parent.tick = 4
x.parent.parent.tick = 4
- print '-',
+ print('-', end=' ')
totGABAWt += sum(x.vec.weight) * x.parent.parent.Gbar
- print 'connectDetailedNeuron: numExc = ', numExc, ', numNMDA=', numNMDA, ', numInh = ', numInh
- print 'connectDetailedNeuron: totWts Glu = ', totGluWt, ', NMDA = ', totNMDAWt, ', GABA = ', totGABAWt
+ print('connectDetailedNeuron: numExc = ', numExc, ', numNMDA=', numNMDA, ', numInh = ', numInh)
+ print('connectDetailedNeuron: totWts Glu = ', totGluWt, ', NMDA = ', totNMDAWt, ', GABA = ', totGABAWt)
#############################################
# Exc-Inh network base class without connections
@@ -447,7 +447,7 @@ class ExcInhNetBase:
numVms = 10
self.plots = moose.Table( '/plotVms', numVms )
## draw numVms out of N neurons
- nrnIdxs = random.sample(range(self.N),numVms)
+ nrnIdxs = random.sample(list(range(self.N)),numVms)
for i in range( numVms ):
moose.connect( self.network.vec[nrnIdxs[i]], 'VmOut', \
self.plots.vec[i], 'input')
@@ -572,7 +572,7 @@ class ExcInhNet(ExcInhNetBase):
## Connections from some Exc neurons to each Exc neuron
## draw excC number of neuron indices out of NmaxExc neurons
- preIdxs = random.sample(range(self.NmaxExc),self.excC)
+ preIdxs = random.sample(list(range(self.NmaxExc)),self.excC)
## connect these presynaptically to i-th post-synaptic neuron
for synnum,preIdx in enumerate(preIdxs):
synidx = i*self.excC+synnum
@@ -625,7 +625,7 @@ class ExcInhNet(ExcInhNetBase):
## Connections from some Inh neurons to each Exc neuron
## draw inhC=incC-excC number of neuron indices out of inhibitory neurons
- preIdxs = random.sample(range(self.NmaxExc,self.N),self.incC-self.excC)
+ preIdxs = random.sample(list(range(self.NmaxExc,self.N)),self.incC-self.excC)
## connect these presynaptically to i-th post-synaptic neuron
for synnum,preIdx in enumerate(preIdxs):
synij = self.synsIE.vec[i].synapse[synnum]
@@ -640,7 +640,7 @@ class ExcInhNet(ExcInhNetBase):
self.synsI.vec[i].numSynapses = self.incC
## draw excC number of neuron indices out of NmaxExc neurons
- preIdxs = random.sample(range(self.NmaxExc),self.excC)
+ preIdxs = random.sample(list(range(self.NmaxExc)),self.excC)
## connect these presynaptically to i-th post-synaptic neuron
for synnum,preIdx in enumerate(preIdxs):
synij = self.synsI.vec[i].synapse[synnum]
@@ -650,7 +650,7 @@ class ExcInhNet(ExcInhNetBase):
synij.weight = self.J # activation = weight
## draw inhC=incC-excC number of neuron indices out of inhibitory neurons
- preIdxs = random.sample(range(self.NmaxExc,self.N),self.incC-self.excC)
+ preIdxs = random.sample(list(range(self.NmaxExc,self.N)),self.incC-self.excC)
## connect these presynaptically to i-th post-synaptic neuron
for synnum,preIdx in enumerate(preIdxs):
synij = self.synsI.vec[i].synapse[ self.excC + synnum ]
@@ -828,7 +828,7 @@ def create_viewer(rdes):
# dendrite.set_colors(moogli.core.Vec4f(173 / 255.0, 216 / 255.0, 230 / 255.0, 1.0))
[shape.set_radius(shape.get_apex_radius() * 4.0) for shape in
- network.groups["spine"].groups["head"].shapes.values()]
+ list(network.groups["spine"].groups["head"].shapes.values())]
# print "Creating LIFS"
soma = network.shapes[rdes.soma.path]
@@ -858,8 +858,8 @@ def create_viewer(rdes):
# print "Creating Viewer"
viewer = moogli.Viewer("viewer") # prelude = prelude, interlude = interlude)
# print "Created Viewer"
- viewer.attach_shapes(network.shapes.values())
- viewer.attach_shapes(lifs.shapes.values())
+ viewer.attach_shapes(list(network.shapes.values()))
+ viewer.attach_shapes(list(lifs.shapes.values()))
# print "Attached Shapes"
view = moogli.View("view")
viewer.attach_view(view)
@@ -873,7 +873,7 @@ if __name__=='__main__':
## Instantiate either ExcInhNetBase or ExcInhNet below
#net = ExcInhNetBase(N=N)
net = ExcInhNet(N=N)
- print net
+ print(net)
moose.le( '/' )
moose.le( '/network' )
rdes = buildRdesigneur()
@@ -916,17 +916,17 @@ if __name__=='__main__':
moose.reinit()
t1 = time.time()
- print 'starting'
+ print('starting')
#moose.start(simtime)
for currTime in np.arange( 0, simtime, updateDt ):
moose.start(updateDt)
lastt = net.network.vec.lastEventTime
lastt = np.exp( 2 * (lastt - currTime ) )
- print currTime, time.time() - t1
+ print(currTime, time.time() - t1)
ret.set_array( lastt )
fig2.canvas.draw()
- print 'runtime, t = ', time.time() - t1
+ print('runtime, t = ', time.time() - t1)
if plotif:
net._plot( fig )
@@ -937,4 +937,4 @@ if __name__=='__main__':
plt.show()
plt.savefig( fname + '.svg', bbox_inches='tight')
print( "Hit 'enter' to exit" )
- raw_input()
+ input()
diff --git a/passive/passive_soma.py b/passive/passive_soma.py
index 11f249b51a75838cd398f7a28ab0afa41fae9941..1a7a74a3a014dec1ea8e03012fa4488401d5ec53 100644
--- a/passive/passive_soma.py
+++ b/passive/passive_soma.py
@@ -56,7 +56,8 @@ def main():
moose.start(t)
time_vector = pylab.linspace(0, t, len(vmtab.vector))
pylab.plot(time_vector, vmtab.vector)
- pylab.savefig('soma_passive.png')
+ pylab.show( )
+ # pylab.savefig('soma_passive.png')
if __name__ == '__main__':
main()
diff --git a/snippets/rxdFuncDiffusion.py b/snippets/rxdFuncDiffusion.py
index b0bb48473e088d1ab95222b723e36f45be93ebc1..142341dc321d79d7f99efd199643d38337b47629 100644
--- a/snippets/rxdFuncDiffusion.py
+++ b/snippets/rxdFuncDiffusion.py
@@ -62,7 +62,8 @@ for t in range( 0, runtime-1, updateDt ):
print("Time = %s " % ( time.time() - t1) )
pylab.ylim( 0, 1.05 )
pylab.legend()
-outfile = '%s.png' % sys.argv[0]
-pylab.savefig( outfile )
-print( '[INFO] Wrote results to %s' % outfile )
+pylab.show( )
+# outfile = '%s.png' % sys.argv[0]
+# pylab.savefig( outfile )
+# print( '[INFO] Wrote results to %s' % outfile )
diff --git a/tutorials/ChemicalBistables/doseResponse.py b/tutorials/ChemicalBistables/doseResponse.py
index 059b5371606130d0b589d747dde6aa8aa045d504..30828faf30142e3e21022ec001ee3e181c515043 100644
--- a/tutorials/ChemicalBistables/doseResponse.py
+++ b/tutorials/ChemicalBistables/doseResponse.py
@@ -111,9 +111,9 @@ def main():
pylab.suptitle('Dose-Reponse Curve for a bistable system')
pylab.legend(loc=3)
- plt.savefig(outputDir + "/" + modelName +"_doseResponse" + ".png")
+ #plt.savefig(outputDir + "/" + modelName +"_doseResponse" + ".png")
plt.show()
- plt.close(fig0)
+ #plt.close(fig0)
quit()
diff --git a/tutorials/ExcInhNetCaPlasticity/ExcInhNet_HigginsGraupnerBrunel2014.py b/tutorials/ExcInhNetCaPlasticity/ExcInhNet_HigginsGraupnerBrunel2014.py
index 07a227bfc69a1845e22c8fec09c375f49c22a5b9..760771e15f919c4f4c7ae011394ea161b3330cbc 100644
--- a/tutorials/ExcInhNetCaPlasticity/ExcInhNet_HigginsGraupnerBrunel2014.py
+++ b/tutorials/ExcInhNetCaPlasticity/ExcInhNet_HigginsGraupnerBrunel2014.py
@@ -672,8 +672,9 @@ def load_plot_Fig5():
#plt.ylabel("# per bin")
fig.tight_layout()
- f.close()
- fig.savefig("HGB2014_Fig5ab_MOOSE.tif",dpi=fig_dpi)
+ # plt.show( )
+ # f.close()
+ # fig.savefig("HGB2014_Fig5ab_MOOSE.tif",dpi=fig_dpi)
def extra_plots(net):
## extra plots apart from the spike rasters